indiv smooths
Megan Lott
10/14/2020
Load all required libraries.
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.6.3## -- Attaching packages ------------------------------------------------------------------------ tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## Warning: package 'ggplot2' was built under R version 3.6.3## Warning: package 'tibble' was built under R version 3.6.3## Warning: package 'readr' was built under R version 3.6.3## Warning: package 'dplyr' was built under R version 3.6.3## Warning: package 'forcats' was built under R version 3.6.3## -- Conflicts --------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(plotly)## Warning: package 'plotly' was built under R version 3.6.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(broom)## Warning: package 'broom' was built under R version 3.6.3Read in raw data from RDS.
raw_data <- readRDS("./n1_n2_cleaned_cases.rds")Make a few small modifications to names and data for visualizations.
final_data <- raw_data %>% mutate(log_copy_per_L = log10(mean_copy_num_L)) %>%
rename(Facility = wrf) %>%
mutate(Facility = recode(Facility,
"NO" = "WRF A",
"MI" = "WRF B",
"CC" = "WRF C"))Seperate the data by gene target to ease layering in the final plot
#make three data layers
only_positives <<- subset(final_data, (!is.na(final_data$Facility)))
only_n1 <- subset(only_positives, target == "N1")
only_n2 <- subset(only_positives, target == "N2")
only_background <<-final_data %>%
select(c(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke)) %>%
group_by(date) %>% summarise_if(is.numeric, mean)
#specify fun colors
background_color <- "#7570B3"
seven_day_ave_color <- "#E6AB02"
marker_colors <- c("N1" = '#1B9E77',"N2" ='#D95F02')#remove facilty C for now
#only_n1 <- only_n1[!(only_n1$Facility == "WRF C"),]
#only_n2 <- only_n2[!(only_n2$Facility == "WRF C"),]
only_n1 <- only_n1[!(only_n1$Facility == "WRF A" & only_n1$date == "2020-11-02"), ]
only_n2 <- only_n2[!(only_n2$Facility == "WRF A" & only_n2$date == "2020-11-02"), ]Build the main plot
#first layer is the background epidemic curve
p1 <- only_background %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~date, y = ~new_cases_clarke,
type = "bar",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Daily Cases: ', new_cases_clarke),
alpha = 0.5,
name = "Daily Reported Cases",
color = background_color,
colors = background_color,
showlegend = FALSE) %>%
layout(yaxis = list(title = "Clarke County Daily Cases", showline=TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
#renders the main plot layer two as seven day moving average
p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~X7_day_ave_clarke,
type = "scatter",
mode = "lines",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Seven-Day Moving Average: ', X7_day_ave_clarke),
name = "Seven Day Moving Average Athens",
line = list(color = seven_day_ave_color),
showlegend = FALSE)
#renders the main plot layer three as positive target hits
p2 <- plotly::plot_ly() %>%
plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
type = "scatter",
mode = "markers",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Facility: ', Facility,
'</br> Target: ', target,
'</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
data = only_n1,
symbol = ~Facility,
marker = list(color = '#1B9E77', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
type = "scatter",
mode = "markers",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Facility: ', Facility,
'</br> Target: ', target,
'</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
data = only_n2,
symbol = ~Facility,
marker = list(color = '#D95F02', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(yaxis = list(title = "SARS CoV-2 Copies/L",
showline = TRUE,
type = "log",
dtick = 1,
automargin = TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
#adds the limit of detection dashed line
p2 <- p2 %>% plotly::add_segments(x = as.Date("2020-03-14"),
xend = ~max(date + 10),
y = 3571.429, yend = 3571.429,
opacity = 0.35,
line = list(color = "black", dash = "dash")) %>%
layout(annotations = list(x = as.Date("2020-03-28"), y = 3.8, xref = "x", yref = "y",
text = "Limit of Detection", showarrow = FALSE))
p1## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## Warning: Ignoring 1 observations p2## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.#seperate n1 and n2 frames by site
#n1
wrf_a_only_n1 <- subset(only_n1, Facility == "WRF A")
wrf_b_only_n1 <- subset(only_n1, Facility == "WRF B")
wrf_c_only_n1 <- subset(only_n1, Facility == "WRF C")
#n2
wrf_a_only_n2 <- subset(only_n2, Facility == "WRF A")
wrf_b_only_n2 <- subset(only_n2, Facility == "WRF B")
wrf_c_only_n2 <- subset(only_n2, Facility == "WRF C")
#rejoin the old data frames then seperate in to averages for each plant.
wrfa_both <- full_join(wrf_a_only_n1, wrf_a_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "cases_cum_clarke", "new_cases_clarke", "X7_day_ave_clarke", "cases_per_100000_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")wrfb_both <- full_join(wrf_b_only_n1, wrf_b_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "cases_cum_clarke", "new_cases_clarke", "X7_day_ave_clarke", "cases_per_100000_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")wrfc_both <- full_join(wrf_c_only_n1, wrf_c_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "cases_cum_clarke", "new_cases_clarke", "X7_day_ave_clarke", "cases_per_100000_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")#get max date
maxdate <- max(wrfa_both$date)
mindate <- min(wrfa_both$date)Build loess smoothing figures figures
This makes the individual plots
#**************************************WRF A PLOT**********************************************
#add trendlines
#extract data from geom_smooth
#both extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_botha <- ggplot(wrfa_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_botha<<-..y..), method = "loess", color = '#1B9E77',
span = 0.6, n = 261)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_botha## `geom_smooth()` using formula 'y ~ x'
fit_botha## [1] 12.82966 12.83675 12.84376 12.85067 12.85748 12.86419 12.87078 12.87725
## [9] 12.88360 12.88981 12.89588 12.90181 12.90759 12.91320 12.91865 12.92393
## [17] 12.92903 12.93395 12.93867 12.94320 12.94752 12.95164 12.95554 12.95923
## [25] 12.96273 12.96604 12.96916 12.97212 12.97490 12.97753 12.98001 12.98235
## [33] 12.98455 12.98663 12.98859 12.99043 12.99217 12.99382 12.99538 12.99686
## [41] 12.99827 12.99962 13.00091 13.00207 13.00303 13.00381 13.00440 13.00481
## [49] 13.00506 13.00515 13.00508 13.00487 13.00451 13.00403 13.00342 13.00269
## [57] 13.00185 13.00091 12.99988 12.99875 12.99755 12.99627 12.99492 12.99352
## [65] 12.99206 12.99056 12.98902 12.98745 12.98586 12.98425 12.98264 12.98060
## [73] 12.97776 12.97419 12.96998 12.96518 12.95987 12.95413 12.94802 12.94161
## [81] 12.93499 12.92822 12.92137 12.91452 12.90773 12.90109 12.89466 12.88851
## [89] 12.88272 12.87736 12.87250 12.86821 12.86349 12.85736 12.84995 12.84137
## [97] 12.83174 12.82119 12.80983 12.79778 12.78517 12.77211 12.75872 12.74512
## [105] 12.73143 12.71777 12.70427 12.69103 12.67819 12.66585 12.65414 12.64318
## [113] 12.63310 12.62400 12.61600 12.60924 12.60383 12.59988 12.59752 12.59686
## [121] 12.59810 12.60121 12.60597 12.61217 12.61960 12.62805 12.63730 12.64716
## [129] 12.65740 12.66781 12.67819 12.68832 12.69799 12.70699 12.71510 12.72282
## [137] 12.73074 12.73888 12.74721 12.75573 12.76444 12.77333 12.78240 12.79163
## [145] 12.80102 12.81056 12.82024 12.83007 12.84003 12.85011 12.86032 12.87064
## [153] 12.88106 12.89158 12.90219 12.91290 12.92569 12.94218 12.96179 12.98393
## [161] 13.00800 13.03342 13.05961 13.08596 13.11190 13.13682 13.16016 13.18130
## [169] 13.19968 13.21469 13.22987 13.24879 13.27095 13.29583 13.32291 13.35168
## [177] 13.38163 13.41224 13.44300 13.47339 13.50291 13.53103 13.55724 13.58103
## [185] 13.60188 13.61929 13.63273 13.64169 13.64565 13.64545 13.64240 13.63679
## [193] 13.62890 13.61898 13.60732 13.59419 13.57986 13.56461 13.54870 13.53242
## [201] 13.51603 13.49981 13.48403 13.46896 13.45488 13.44060 13.42485 13.40779
## [209] 13.38955 13.37028 13.35012 13.32922 13.30773 13.28579 13.26355 13.24115
## [217] 13.21873 13.19598 13.17244 13.14813 13.12301 13.09709 13.07035 13.04277
## [225] 13.01436 12.98509 12.95495 12.92394 12.89204 12.85925 12.82554 12.79078
## [233] 12.75487 12.71791 12.68000 12.64123 12.60170 12.56149 12.52071 12.47944
## [241] 12.43760 12.39504 12.35174 12.30772 12.26297 12.21749 12.17128 12.12433
## [249] 12.07665 12.02823 11.97908 11.92919 11.87855 11.82718 11.77506 11.72220
## [257] 11.66860 11.61425 11.55915 11.50330 11.44670#assign fits to a vector
both_trenda <- fit_botha
#extract y min and max for each
limits_botha <- ggplot_build(extract_botha)$data## `geom_smooth()` using formula 'y ~ x'limits_botha <- as.data.frame(limits_botha)
both_ymina <- limits_botha$ymin
both_ymaxa <- limits_botha$ymax
#reassign dataframes (just to be safe)
work_botha <- wrfa_both
#fill in missing dates to smooth fits
work_botha <- work_botha %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_botha <- work_botha$date
#create a new smooth dataframe to layer
smooth_frame_botha <- data.frame(date_vec_botha, both_trenda, both_ymina, both_ymaxa)#WRF A
#plot smooth frames
p_wrf_a <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_botha, y = ~both_trenda,
data = smooth_frame_botha,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_botha,
'</br> Median Log Copies: ', round(both_trenda, digits = 2)),
line = list(color = '#1B9E77', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_botha, ymin = ~both_ymina, ymax = ~both_ymaxa,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_botha, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxa, digits = 2),
'</br> Min Log Copies: ', round(both_ymina, digits = 2)),
name = "",
fillcolor = '#1B9E77',
line = list(color = '#1B9E77')) %>%
layout(yaxis = list(title = "Total Log SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF A") %>%
plotly::add_segments(x = as.Date("2020-06-24"),
xend = as.Date("2020-06-24"),
y = ~min(both_ymina), yend = ~max(both_ymaxa),
opacity = 0.35,
name = "Bars Repoen",
hoverinfo = "text",
text = "</br> Bars Reopen",
"</br> 2020-06-24",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-07-09"),
xend = as.Date("2020-07-09"),
y = ~min(both_ymina), yend = ~max(both_ymaxa),
opacity = 0.35,
name = "Mask Mandate",
hoverinfo = "text",
text = "</br> Mask Mandate",
"</br> 2020-07-09",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-08-20"),
xend = as.Date("2020-08-20"),
y = ~min(both_ymina), yend = ~max(both_ymaxa),
opacity = 0.35,
name = "</br> Classes Begin",
"</br> 2020-08-20",
hoverinfo = "text",
text = "Classes Begin",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-10-03"),
xend = as.Date("2020-10-03"),
y = ~min(both_ymina), yend = ~max(both_ymaxa),
opacity = 0.35,
name = "</br> First Home Football Game",
"</br> 2020-10-03",
hoverinfo = "text",
text = "First Home Football Game",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfa_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#1B9E77', size = 6, opacity = 0.65))
p_wrf_asave(p_wrf_a, file = "./plotly_objs/p_wrf_a.rda")#**************************************WRF B PLOT**********************************************
#add trendlines
#extract data from geom_smooth
#both extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_bothb <- ggplot(wrfb_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_bothb<<-..y..), method = "loess", color = '#D95F02',
span = 0.6, n = 261)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_bothb## `geom_smooth()` using formula 'y ~ x'
fit_bothb## [1] 12.51176 12.51286 12.51394 12.51500 12.51604 12.51707 12.51810 12.51913
## [9] 12.52016 12.52121 12.52229 12.52338 12.52451 12.52568 12.52689 12.52815
## [17] 12.52947 12.53085 12.53230 12.53382 12.53542 12.53711 12.53887 12.54067
## [25] 12.54252 12.54439 12.54630 12.54823 12.55017 12.55213 12.55408 12.55604
## [33] 12.55800 12.55994 12.56186 12.56376 12.56562 12.56746 12.56925 12.57099
## [41] 12.57268 12.57431 12.57588 12.57740 12.57888 12.58033 12.58175 12.58314
## [49] 12.58451 12.58585 12.58718 12.58849 12.58978 12.59106 12.59232 12.59358
## [57] 12.59484 12.59609 12.59733 12.59858 12.59983 12.60109 12.60236 12.60363
## [65] 12.60492 12.60622 12.60754 12.60888 12.61024 12.61163 12.61304 12.61462
## [73] 12.61651 12.61868 12.62108 12.62370 12.62651 12.62946 12.63255 12.63573
## [81] 12.63897 12.64225 12.64554 12.64880 12.65201 12.65514 12.65815 12.66102
## [89] 12.66373 12.66623 12.66850 12.67051 12.67127 12.66995 12.66670 12.66168
## [97] 12.65506 12.64700 12.63767 12.62722 12.61582 12.60363 12.59081 12.57754
## [105] 12.56397 12.55026 12.53658 12.52309 12.50995 12.49733 12.48539 12.47429
## [113] 12.46419 12.45527 12.44768 12.44158 12.43714 12.43451 12.43388 12.43539
## [121] 12.43860 12.44290 12.44818 12.45434 12.46128 12.46889 12.47708 12.48574
## [129] 12.49478 12.50407 12.51354 12.52306 12.53255 12.54394 12.55888 12.57686
## [137] 12.59735 12.61983 12.64377 12.66864 12.69393 12.71911 12.74365 12.76704
## [145] 12.78874 12.80823 12.82499 12.84278 12.86488 12.89012 12.91730 12.94524
## [153] 12.97275 12.99864 13.02172 13.04080 13.05890 13.07952 13.10218 13.12642
## [161] 13.15175 13.17770 13.20379 13.22955 13.25450 13.27817 13.30009 13.31977
## [169] 13.33674 13.35054 13.36322 13.37703 13.39174 13.40712 13.42295 13.43898
## [177] 13.45500 13.47077 13.48606 13.50065 13.51431 13.52680 13.53790 13.54737
## [185] 13.55500 13.56054 13.56377 13.56446 13.56239 13.55772 13.55090 13.54212
## [193] 13.53156 13.51940 13.50582 13.49100 13.47513 13.45839 13.44095 13.42300
## [201] 13.40473 13.38630 13.36792 13.34975 13.33197 13.31378 13.29431 13.27368
## [209] 13.25199 13.22936 13.20590 13.18173 13.15697 13.13171 13.10609 13.08020
## [217] 13.05417 13.02771 13.00046 12.97242 12.94361 12.91403 12.88369 12.85260
## [225] 12.82077 12.78820 12.75490 12.72088 12.68614 12.65071 12.61457 12.57764
## [233] 12.53983 12.50118 12.46174 12.42156 12.38066 12.33909 12.29690 12.25413
## [241] 12.21074 12.16665 12.12188 12.07642 12.03027 11.98343 11.93590 11.88768
## [249] 11.83876 11.78915 11.73885 11.68786 11.63617 11.58378 11.53070 11.47692
## [257] 11.42244 11.36727 11.31140 11.25483 11.19756#assign fits to a vector
both_trendb <- fit_bothb
#extract y min and max for each
limits_bothb <- ggplot_build(extract_bothb)$data## `geom_smooth()` using formula 'y ~ x'limits_bothb <- as.data.frame(limits_bothb)
both_yminb <- limits_bothb$ymin
both_ymaxb <- limits_bothb$ymax
#reassign dataframes (just to be safe)
work_bothb <- wrfb_both
#fill in missing dates to smooth fits
work_bothb <- work_bothb %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_bothb <- work_bothb$date
#create a new smooth dataframe to layer
smooth_frame_bothb <- data.frame(date_vec_bothb, both_trendb, both_yminb, both_ymaxb)#WRF B
#plot smooth frames
p_wrf_b <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_bothb, y = ~both_trendb,
data = smooth_frame_bothb,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothb,
'</br> Median Log Copies: ', round(both_trendb, digits = 2)),
line = list(color = '#D95F02', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_bothb, ymin = ~both_yminb, ymax = ~both_ymaxb,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothb, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxb, digits = 2),
'</br> Min Log Copies: ', round(both_yminb, digits = 2)),
name = "",
fillcolor = '#D95F02',
line = list(color = '#D95F02')) %>%
layout(yaxis = list(title = "Total Log SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF B") %>%
plotly::add_segments(x = as.Date("2020-06-24"),
xend = as.Date("2020-06-24"),
y = ~min(both_yminb), yend = ~max(both_ymaxb),
opacity = 0.35,
name = "Bars Repoen",
hoverinfo = "text",
text = "</br> Bars Reopen",
"</br> 2020-06-24",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-07-09"),
xend = as.Date("2020-07-09"),
y = ~min(both_yminb), yend = ~max(both_ymaxb),
opacity = 0.35,
name = "Mask Mandate",
hoverinfo = "text",
text = "</br> Mask Mandate",
"</br> 2020-07-09",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-08-20"),
xend = as.Date("2020-08-20"),
y = ~min(both_yminb), yend = ~max(both_ymaxb),
opacity = 0.35,
name = "</br> Classes Begin",
"</br> 2020-08-20",
hoverinfo = "text",
text = "Classes Begin",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-10-03"),
xend = as.Date("2020-10-03"),
y = ~min(both_yminb), yend = ~max(both_ymaxb),
opacity = 0.35,
name = "</br> First Home Football Game",
"</br> 2020-10-03",
hoverinfo = "text",
text = "First Home Football Game",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfb_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#D95F02', size = 6, opacity = 0.65))
p_wrf_bsave(p_wrf_b, file = "./plotly_objs/p_wrf_b.rda")#**************************************WRF C PLOT********************************************** #add trendlines #extract data from geom_smooth # *********************************span 0.6*********************************** #*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_bothc <- ggplot(wrfc_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_bothc<<-..y..), method = "loess", color = '#E7298A',
span = 0.6, n = 261)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_bothc## `geom_smooth()` using formula 'y ~ x'
fit_bothc## [1] 11.54860 11.56910 11.58936 11.60934 11.62905 11.64846 11.66756 11.68635
## [9] 11.70481 11.72292 11.74068 11.75807 11.77508 11.79170 11.80791 11.82370
## [17] 11.83906 11.85398 11.86844 11.88243 11.89594 11.90896 11.92147 11.93347
## [25] 11.94493 11.95585 11.96621 11.97600 11.98521 11.99381 12.00176 12.00911
## [33] 12.01589 12.02213 12.02785 12.03309 12.03788 12.04225 12.04623 12.04984
## [41] 12.05313 12.05612 12.05883 12.06131 12.06358 12.06568 12.06762 12.06945
## [49] 12.07119 12.07287 12.07390 12.07371 12.07236 12.06994 12.06651 12.06215
## [57] 12.05694 12.05094 12.04422 12.03687 12.02896 12.02055 12.01173 12.00255
## [65] 11.99311 11.98347 11.97370 11.96387 11.95407 11.94436 11.93482 11.92552
## [73] 11.91653 11.90793 11.89978 11.89217 11.88517 11.87884 11.87144 11.86136
## [81] 11.84889 11.83432 11.81796 11.80010 11.78103 11.76104 11.74043 11.71950
## [89] 11.69854 11.67785 11.65772 11.63844 11.62031 11.60362 11.58867 11.57576
## [97] 11.56517 11.55721 11.55217 11.54834 11.54389 11.53893 11.53356 11.52787
## [105] 11.52197 11.51595 11.50991 11.50396 11.49820 11.49272 11.48763 11.48303
## [113] 11.47901 11.47568 11.47313 11.47147 11.47080 11.47121 11.47282 11.47571
## [121] 11.47998 11.48575 11.49310 11.50214 11.51297 11.52568 11.54313 11.56700
## [129] 11.59566 11.62749 11.66087 11.69418 11.72578 11.75405 11.77737 11.79934
## [137] 11.82443 11.85234 11.88275 11.91535 11.94983 11.98587 12.02316 12.06139
## [145] 12.10025 12.13943 12.17861 12.21747 12.25572 12.29303 12.32910 12.36360
## [153] 12.39624 12.42669 12.45465 12.47980 12.50414 12.52971 12.55622 12.58340
## [161] 12.61096 12.63861 12.66609 12.69310 12.71937 12.74462 12.76857 12.79092
## [169] 12.81142 12.82976 12.84719 12.86504 12.88316 12.90142 12.91967 12.93778
## [177] 12.95560 12.97301 12.98984 13.00598 13.02127 13.03558 13.04877 13.06069
## [185] 13.07122 13.08020 13.08750 13.09298 13.09650 13.09858 13.09980 13.10015
## [193] 13.09960 13.09812 13.09570 13.09231 13.08793 13.08252 13.07607 13.06856
## [201] 13.05995 13.05023 13.03937 13.02735 13.01414 13.00013 12.98566 12.97060
## [209] 12.95482 12.93822 12.92066 12.90202 12.88219 12.86103 12.83844 12.81429
## [217] 12.78845 12.76127 12.73314 12.70403 12.67392 12.64280 12.61064 12.57742
## [225] 12.54312 12.50770 12.47116 12.43347 12.39461 12.35454 12.31327 12.27089
## [233] 12.22753 12.18315 12.13772 12.09120 12.04357 11.99480 11.94484 11.89366
## [241] 11.84130 11.78779 11.73314 11.67735 11.62042 11.56235 11.50314 11.44280
## [249] 11.38132 11.31871 11.25496 11.19009 11.12408 11.05695 10.98869 10.91931
## [257] 10.84880 10.77717 10.70442 10.63054 10.55555#assign fits to a vector
both_trendc <- fit_bothc
#extract y min and max for each
limits_bothc <- ggplot_build(extract_bothc)$data## `geom_smooth()` using formula 'y ~ x'limits_bothc <- as.data.frame(limits_bothc)
both_yminc <- limits_bothc$ymin
both_ymaxc <- limits_bothc$ymax
#reassign dataframes (just to be safe)
work_bothc <- wrfc_both
#fill in missing dates to smooth fits
work_bothc <- work_bothc %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_bothc <- work_bothc$date
#create a new smooth dataframe to layer
smooth_frame_bothc <- data.frame(date_vec_bothc, both_trendc, both_yminc, both_ymaxc)#WRF C
#plot smooth frames
p_wrf_c <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_bothc, y = ~both_trendc,
data = smooth_frame_bothc,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothc,
'</br> Median Log Copies: ', round(both_trendc, digits = 2)),
line = list(color = '#E7298A', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_bothc, ymin = ~both_yminc, ymax = ~both_ymaxc,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothc, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxc, digits = 2),
'</br> Min Log Copies: ', round(both_yminc, digits = 2)),
name = "",
fillcolor = '#E7298A',
line = list(color = '#E7298A')) %>%
layout(yaxis = list(title = "Total Log SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF C") %>%
plotly::add_segments(x = as.Date("2020-06-24"),
xend = as.Date("2020-06-24"),
y = ~min(both_yminc), yend = ~max(both_ymaxc),
opacity = 0.35,
name = "Bars Repoen",
hoverinfo = "text",
text = "</br> Bars Reopen",
"</br> 2020-06-24",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-07-09"),
xend = as.Date("2020-07-09"),
y = ~min(both_yminc), yend = ~max(both_ymaxc),
opacity = 0.35,
name = "Mask Mandate",
hoverinfo = "text",
text = "</br> Mask Mandate",
"</br> 2020-07-09",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-08-20"),
xend = as.Date("2020-08-20"),
y = ~min(both_yminc), yend = ~max(both_ymaxc),
opacity = 0.35,
name = "</br> Classes Begin",
"</br> 2020-08-20",
hoverinfo = "text",
text = "Classes Begin",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_segments(x = as.Date("2020-10-03"),
xend = as.Date("2020-10-03"),
y = ~min(both_yminc), yend = ~max(both_ymaxc),
opacity = 0.35,
name = "</br> First Home Football Game",
"</br> 2020-10-03",
hoverinfo = "text",
text = "First Home Football Game",
showlegend = FALSE,
line = list(color = "black", dash = "dash")) %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfc_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#E7298A', size = 6, opacity = 0.65))
p_wrf_csave(p_wrf_c, file = "./plotly_objs/p_wrf_c.rda")save(wrfa_both, file = "./plotly_objs/wrfa_both.rda")
save(wrfb_both, file = "./plotly_objs/wrfb_both.rda")
save(wrfc_both, file = "./plotly_objs/wrfc_both.rda")save(date_vec_botha, file = "./plotly_objs/date_vec_botha.rda")
save(date_vec_bothb, file = "./plotly_objs/date_vec_bothb.rda")
save(date_vec_bothc, file = "./plotly_objs/date_vec_bothc.rda")save(both_ymina, file = "./plotly_objs/both_ymina.rda")
save(both_ymaxa, file = "./plotly_objs/both_ymaxa.rda")
save(both_yminb, file = "./plotly_objs/both_yminb.rda")
save(both_ymaxb, file = "./plotly_objs/both_ymaxb.rda")
save(both_yminc, file = "./plotly_objs/both_yminc.rda")
save(both_ymaxc, file = "./plotly_objs/both_ymaxc.rda")